Meta-Learning for Semi-Supervised Few-Shot Classification

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Abstract

In few-shot classification, we are interested in learning algorithms that train a
classifier from only a handful of labeled examples. Recent progress made in
few-shot classification has featured meta-learning, in which a parameterized model
for a learning algorithm is defined and trained on episodes representing different
classification problems, each with a small labeled training set and its
corresponding test set. In this work, we advance this few-shot classification
paradigm towards a scenario where unlabeled examples are also available within each
episode. We consider two situations: one where all unlabeled examples are assumed
to belong to the same set of classes as the labeled examples of the episode, as
well as the more realistic situation where examples from other {\it distractor}
classes are also provided. To address this paradigm, we propose novel extensions of
prototypical networks (Snell et al. 2017) that are augmented with the ability to
use unlabeled examples when producing prototypes. These models are trained in an
end-to-end way on episodes, to learn to leverage the unlabeled examples
successfully. We evaluate these methods on versions of the Omniglot and
mini-ImageNet benchmarks, adapted to this new framework augmented with unlabeled
examples. We also propose a new split of ImageNet. Our experiments confirm that our
prototypical networks can learn to improve their predictions due to unlabeled
examples, much like a semi-supervised algorithm would.